System and method for fan blade rotor disk and gear inspection

ABSTRACT

A system for component inspection comprising at least one sensor configured to capture sensor data of the component; and a processor coupled to the at least one sensor, the processor comprising at least one model configured to separate the sensor data into a normal category and an abnormal category.

BACKGROUND

The present disclosure is directed to an automated optical inspectionsystem. Particularly, the disclosure is directed to an automated opticalinspection system for machinery components with particular applicationto turbine fan blades, turbine blades, turbine disks, turbine vaneassemblies, and turbine gears, using image, video, or 3D sensing anddamage detection analytics. Even more particularly, the turbine may be agas turbine for power generation, air craft auxiliary power, aircraftpropulsion, and the like.

Gas turbine engine components, such as blades and vanes, may sufferirregularities from manufacturing or wear and damage during operation,for example, due to erosion, hot corrosion (sulfidation), cracks, dents,nicks, gouges, and other damage, such as from foreign object damage.Other gas turbine engine components, such as rotor disks and gears, maysuffer irregularities from manufacturing or damage from use, forexample, such as wear, fretting and fatigue cracking. Detecting thisdamage may be achieved by images, videos, or 3D sensing for aircraftengine blade inspection, power turbine blade inspection, aircraft enginedisk inspection, aircraft engine vane assembly inspection, gearinspection, internal inspection of mechanical devices, and the like. Avariety of techniques for inspecting by use of images, videos, or 3Dsensing may include capturing and displaying images, videos, or 3D datato human inspectors for manual defect detection and interpretation.Human inspectors may then decide whether any defect exists within thoseimages, videos, or 3D data. When human inspectors look at many similarimages, videos, or 3D data of very similar blades, vanes, slots, gearteeth, and the like of an engine stage, or any like subcomponents of adevice, they may not detect defects, for example, because of fatigue ordistraction experienced by the inspector. Missing a defect may lead tocustomer dissatisfaction, transportation of an expensive engine back toservice centers, lost revenue, or even engine failure. Additionally,manual inspection of components may be time consuming and expensive.

SUMMARY

In accordance with the present disclosure, there is provided system forcomponent inspection comprising at least one sensor configured tocapture sensor data of the component; and a processor coupled to the atleast one sensor, the processor comprising at least one model configuredto separate the sensor data into a normal category and an abnormalcategory.

In another and alternative embodiment, the model comprises at least oneof a statistical model, an empirical model, a learned model, a priorcondition model, and a design model.

In another and alternative embodiment, the system further comprises atangible, non-transitory memory configured to communicate with theprocessor, the tangible, non-transitory memory having instructionsstored therein that, in response to execution by the processor, causethe processor to perform operations comprising: receiving, by theprocessor, sensor data for the component from the at least one sensor;organizing, by the processor, the sensor data into a matrix, whereineach frame of the sensor data comprises a single column in the matrix;separating, by the processor, the matrix into at least one of a low-rankpart and a sparse part, wherein a linear combination of the low-rankpart columns represents an undamaged component; and determining, by theprocessor, defects in the component based on the sparse part.

In another and alternative embodiment, the at least one sensor comprisesan optical system configured for high spatial resolution and large depthof field.

In another and alternative embodiment, the system further comprises atangible, non-transitory memory configured to communicate with theprocessor, the tangible, non-transitory memory having instructionsstored therein that, in response to execution by the processor, causethe processor to perform operations comprising: receiving, by theprocessor, sensor data for the component from the at least one sensor;organizing, by the processor, the sensor data into a tensor, whereineach frame of the sensor data comprises a lower-dimensional portion inthe tensor; separating, by the processor, the tensor into at least oneof a normal part and an abnormal part, wherein a linear combination ofthe normal part represents an undamaged component; and determining, bythe processor, defects in the component based on the abnormal part.

In another and alternative embodiment, the at least one sensor comprisesa depth sensing system configured for high spatial resolution and largerange.

In another and alternative embodiment, the processor modifies the sensordata according to a dynamic model of rotational motion duringinspection.

In another and alternative embodiment, the processor comprisesinstructions selected from the group consisting of a Bayesianestimation, a support vector machine (SVM), a decision tree, deep neuralnetwork, recurrent ensemble learning machine, and comparison to athreshold.

In another and alternative embodiment, the component comprises radiallyarranged, substantially similar subcomponents.

In another and alternative embodiment, the component is selected fromthe group consisting of a gas turbine engine disk, a vane assembly, agear, and a fan.

In accordance with the present disclosure, there is provided a methodfor inspection of a component, comprises aligning at least one sensor tocapture sensor data of a component; coupling a processor to the at leastone sensor, the processor comprising at least one model; and separatingthe sensor data into a normal category and an abnormal category.

In another and alternative embodiment, the processor performs operationscomprises receiving sensor data for the component from the at least onesensor; organizing the sensor data into a matrix, wherein each frame ofthe sensor data comprises a single column in the matrix; separating thematrix into at least one of a low-rank part and a sparse part, wherein alinear combination of the low-rank part columns represents an undamagedcomponent; and determining defects in the component based on the sparsepart.

In another and alternative embodiment, the processor performs operationscomprising receiving sensor data for the component from the at least onesensors; organizing the sensor data into a tensor, wherein each frame ofthe sensor data comprises a lower-dimensional portion in the tensor;separating the tensor into at least one of a normal part and an abnormalpart, wherein a linear combination of the normal part represents anundamaged component; and determining defects in the component based onthe abnormal part.

In another and alternative embodiment, the at least one sensor comprisesan optical system configured for high spatial resolution and large depthof field.

In another and alternative embodiment, the at least one sensor comprisesa depth sensing system configured for high spatial resolution and largerange.

In another and alternative embodiment, the at least one model comprisesat least one of a statistical model, an empirical model, a learnedmodel, a prior condition model, and a design model.

In another and alternative embodiment, the processor modifies the sensordata according to a dynamic model of rotational motion duringinspection.

In another and alternative embodiment, the processor comprisesinstructions selected from the group consisting of a Bayesianestimation, a support vector machine (SVM), a decision tree, deep neuralnetwork, recurrent ensemble learning machine, and comparison to athreshold.

A specifically designed camera system comprising a focal plane array(FPA), aperture, and optics is aligned to simultaneously image thepressure face of an entire broached slot or gear tooth at highresolution and in sharp focus. The automated optical inspection systemutilizes image analytics using one or more images to detect machining oroperational damage. When using one image, the inspection system utilizesone or more of image enhancement, edge detection, frame differencingfrom a known-good image (or model), and the like, wherein the framedifferencing includes one or more of registration, cross correlation,normalization, and the like. The image enhancement may include one ormore of histogram equalization, glare reduction, morphologicalfiltering, and the like.

When using more than one image, the disk, gear, fan blade assembly, vaneassembly, or component may be rotated and multiple images are taken atdifferent rotation angles. The automated optical inspection system maythen utilize Robust Principle Components Analysis (RPCA) optionally withlow-order dynamic models of rotational motion during inspection, andstatistical image analysis to automatically detect possible defects.RPCA organizes the images/video frames in a matrix D, where eachimage/frame is one column, and then separates D into a low-rank part Aand sparse part E (the matrix A essentially captures a non-damage modelof the component under inspection and the damaged component, if any, isin the residual matrix E). The sparse part contains possible defects.The low-rank part is determined by the minimizing the matrix nuclearnorm which is the convex relaxation of rank

Other details of the automated optical inspection system are set forthin the following detailed description and the accompanying drawingswherein like reference numerals depict like elements.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of an exemplary inspection system inaccordance with various embodiments.

FIG. 2 is a process map of an exemplary inspection system in accordancewith various embodiments.

FIG. 3 is a schematic diagram of an exemplary disk inspection system.

FIGS. 4a-4d illustrate exemplary camera imagery, a disk shape model, andstatistical analysis to detect damage.

DETAILED DESCRIPTION

Referring to FIG. 1, a schematic illustration of an automated inspectionsystem 10 for detecting a defect or damage to a component 20 is shown,in accordance with various embodiments. Component 20 of FIG. 1 depictsone of many broached slots arranged radially around the periphery of anexemplary turbine engine disk (not depicted). Inspection system 10 maybe configured to perform imaging of a component 20. Component 20 mayinclude a component on an aircraft, such as an engine component, such asa fan blade, a turbine blade, a turbine vane assembly, a disk, or agear. Component 20 may be scanned or sensed by one or more sensors 12 toobtain data 14 about the component 20. Data 14 may be obtained, forexample, from a specialized camera system configured to obtain highresolution and large depth of field. In various embodiments, data 14 maybe obtained by rotating, panning, or positioning the sensor(s) 12relative to the component 20 to capture data 14 from multiple viewpointangles, perspectives, and/or depths. Further, the component 20 may berotated or positioned relative to the sensor(s) 12 to obtain data 14from multiple viewpoints, perspectives, and/or depths. This isparticularly beneficial when component 20 comprises radially arrangedsubcomponents such as airfoils in a fan or vane assembly, slots in adisk, teeth in a gear, and the like. The rotation of component 20 may besuch that a like spatial relationship of a subcomponent to sensor(s) 12is achieved prior to capturing data. An array of sensors 12 positionedaround component 20 may be used to obtain data 14 from multipleviewpoints. Thus, either of the sensor(s) 12 or component 20 may bemoved or positioned relative to the other and relative to variousdirections or axes of a coordinate system to obtain sensor informationfrom various viewpoints, perspectives, and/or depths. Further, sensor 12may scan, sense, or capture information from a single position relativeto component 20.

In an exemplary embodiment, the sensor 12 can be a one-dimensional (1D),2D, or 3D camera or camera system; a 1D, 2D, or 3D depth sensor or depthsensor system; and/or a combination and/or array thereof. Sensor 12 maybe operable in the electromagnetic or acoustic spectrum capable ofproducing a point cloud, occupancy grid or depth map of thecorresponding dimension(s). Sensor 12 may provide variouscharacteristics of the sensed electromagnetic or acoustic spectrumincluding intensity, spectral characteristics, polarization, etc. Invarious embodiments, sensor 12 may include a distance, range, and/ordepth sensing device. Various depth sensing sensor technologies anddevices include, but are not limited to, a structured light measurement,phase shift measurement, time of flight measurement, stereotriangulation device, sheet of light triangulation device, light fieldcameras, coded aperture cameras, computational imaging techniques,simultaneous localization and mapping (SLAM), imaging radar, imagingsonar, echolocation, laser radar, scanning light detection and ranging(LIDAR), flash LIDAR, or a combination comprising at least one of theforegoing. Different technologies can include active (transmitting andreceiving a signal) or passive (only receiving a signal) and may operatein a band of the electromagnetic or acoustic spectrum such as visual,infrared, ultrasonic, etc. In various embodiments, sensor 12 may beoperable to produce depth from defocus, a focal stack of images, orstructure from motion.

In various embodiments, sensor 12 may include a structured light linesensor, a linear image sensor, or other 1D sensor. Further, sensor 12may include a 2D sensor, and inspection system 10 may extract 1Dinformation from the 2D sensor data. 2D data 14 may be synthesized byprocessor 16 from multiple 1D data 14 from a 1D sensor 12 or frommultiple 1D data 14 extracted from a 2D sensor 12. The extraction of 1Ddata 14 from 2D data 14 may include retaining only data that is infocus. Even further, sensor 12 may include a position and/or orientationsensor such as an inertial measurement unit (IMU) that may provideposition and/or orientation information about component 20 with respectto a coordinate system or sensor 12. The position and/or orientationinformation may be beneficially employed in synthesizing 2D data from 1Ddata, or in aligning 1D, 2D or 3D information to a reference model asdiscussed elsewhere herein.

Data 14 from sensor(s) 12 may be transmitted to one or more processors16 (e.g., computer systems having a central processing unit and memory)for recording, processing and storing the data received from sensors 12.Processor 16 may include a general-purpose processor, a digital signalprocessor (DSP), an application specific integrated circuit (ASIC), afield programmable gate array (FPGA) or other programmable logic device,discrete gate or transistor logic, discrete hardware components, or anycombination thereof. Processor 16 may be in communication (such aselectrical communication) with sensors 12 and may be configured toreceive input, such as images and/or depth information from sensors 12.Processor 16 may receive data 14 about component 20 captured andtransmitted by the sensor(s) 12 via a communication channel. Uponreceiving the data 14, the processor 16 may process data 14 from sensors12 to determine if damage or defects are present on the component 20.

In various embodiments, processor 16 may receive or construct image or3D information 30 corresponding to the component 20. The construction of3D information from 1D or 2D information may include tiling, mosaicking,stereopsis, structure from motion, structure from multiple viewpoints,simultaneous localization and mapping, and the like. Processor 16 mayfurther include a reference model 22 stored, for example, in memory ofprocessor 16. Reference model 22 may be generated from a CAD model,and/or information, such as from a scan or information of an originalcomponent or an undamaged component. Reference model 22 may be atheoretical model or may be based on historical or current informationabout component 20. In particular, reference model 22 may be derivedfrom the current image data 14. Reference model 22 may be adjusted andupdated as component 20 and/or similar components are scanned andinspected. Thus, reference model 22 may be a learned model of acomponent and may include, for example, information including shape andsurface features of the component.

In various embodiments, processor 16 of inspection system 10 mayclassify the damage and determine the probability of damage and/or ifthe damage meets or exceeds a threshold 24. Threshold 24 may be an inputparameter, may be based on reference model 22, may be from user input,and the like. Processor 16 may provide an output 26 to a user interface28 indicating the status of the component 20. User interface 28 mayinclude a display. Inspection system 10 may display an indication of thedefect to component 20, which may include an image and/or a report. Inaddition to reporting any defects in the component, output 26 may alsorelay information about the type of defect, the location of the defect,size of the defect, etc. If defects are found in the inspected component20, an indicator may be displayed on user interface 28 to alertpersonnel or users of the defect.

With reference to FIG. 2, a method 200 for detecting defects isprovided, in accordance with various embodiments. Processor 16 may becapable of carrying out the steps of FIG. 2. One or more sensors 12 maycapture data about a component 20. Method 200, performed by processor 16of inspection system 10, may include receiving data from a sensor/camera(step 202). Method 200 may include generating information from thesensor data (step 204). The information may correspond to the component.Method 200 may include determining a sparse part and a low-rank part ofthe sensor data (step 206). Step 206 may further include aligning thesensor data or information with a reference model. Method 200 mayfurther include determining a feature dissimilarity between theinformation and the reference model (step 208), classifying the featuredissimilarity (step 210), determining damage (step 212), and displayingan output (step 214).

Step 202 may further comprise receiving 1D or 2D data, from a sensor 12.In various embodiments, information is received from one or more sensors12, which may be sensors. In receiving data 14 from a sensor, theinspection system 10 may capture depth points of component 20 andrecreate precisely, the actual surfaces of component 20, therebygenerating a complete point cloud or a partial point cloud. In anexemplary embodiment, the entire forward surface of a gas turbine enginefan blade can be captured.

Step 204 may comprise producing a point cloud or occupancy grid, apartial point cloud, a model derived from a point cloud, depth map,other depth information, 1D information, and/or 2D information. A pointcloud or occupancy grid may include a plurality of points or coordinatesin a coordinate system having three dimensions, such as an xyzcoordinate system or polar coordinate system. A partial point cloud mayinclude a plurality of points or coordinates in a coordinate system,where the sensor data is collected from a single viewpoint or a limitedset of viewpoints. A model derived from a point cloud may include amodified point cloud which has been processed to connect various pointsin the point cloud in order to approximate or functionally estimate thetopological surface of the component. A depth map may reflect pointsfrom a point cloud that can be seen from a particular viewpoint. A depthmap may be created by assuming a particular viewpoint of a point cloudin the coordinate system of the point cloud.

Step 204 may further comprise constructing a complete image or pointcloud of the component 20 by mosaicking information from multiplesensors 12 or multiple viewpoints. Step 204 may comprise merging data 14from multiple viewpoints. In various embodiments, step 204 may comprisemerging a first data from a 1D sensor and a second data from a 2D sensorand processing the 1D and 2D data to produce information 30.

In various embodiments, step 204 may comprise computing first data froma first 2D sensor and second data from a second 2D sensor. Processor 16may receive a plurality of 2D sensor data and merge the 2D sensor datato generate a focal stack of 2D sensor data. The focal stack, i.e.multiple layers of 2D sensor data, may produce a volume of data to formthe information 30, which may be a representation of the component.

Step 206 may further comprise of aligning the information, such as apoint cloud, by an iterative closest point (ICP) algorithm modified tosuppress misalignment from damage areas of the component 20. Thealignment may be performed by an optimization method, i.e., minimizingan objective function over a dataset, which may include mathematicalterms in the ICP objective function or constraints to reject features ordamage as outliers. The alignment may be performed by a modification toa random sample consensus (RANSAC) algorithm, scale-invariant featuretransform (SIFT), speeded up robust feature (SURF), or other suitablealignment method. Step 206 may further include comparing the 3Dinformation 30 to the reference model 22 to align the features from theinformation 30 with the reference model 22 by identifying affine and/orscale invariant features, diffeomorphic alignment/scale cascadedalignment, and the like. Step 206 may further include registering thefeatures.

Step 208 may further comprise computing features, such as surface andshape characteristics, of the component 20 by methods to identify andextract features. For example, processor 16 may determine differences ordissimilarities between the information 30 and the reference model 22.Step 208 may further comprise identifying features and determiningdifferences or dissimilarities between the identified features in theinformation 30 and the reference model 22 using a statistical algorithmsuch as a histogram of oriented gradients in 2D or 3D (HoG, HoG3D), 3DZernike moments, or other algorithms. In a HoG3D method, processor 16may define the orientation of edges and surfaces of 3D information 30 bydividing the 3D information 30 into portions or cells and assigning toeach cell a value, where each point or pixel contributes a weightedorientation or gradient to the cell value. By grouping cells andnormalizing the cell values, a histogram of the gradients can beproduced and used to extract or estimate information about an edge or asurface of the component 20. Thus, the features of the information 30,such as surface and edge shapes, may be identified. Other algorithms,such as 3D Zernike moments, may similarly be used to recognize featuresin 3D information 30 by using orthogonal moments to reconstruct, forexample, surface and edge geometry of component 20. Step 208 may furthercomprise determining differences or dissimilarities between theidentified features in the 3D information 30 and the reference model 22.The dissimilarities may be expressed, for example, by the distancebetween two points or vectors. Other approaches to expressingdissimilarities may include computing mathematical models of information30 and reference model 22 in a common basis (comprising modes) andexpressing the dissimilarity as a difference of coefficients of thebasis functions (modes). Differences or dissimilarities between the 3Dinformation 30 and the reference model 22 may represent various types ofdamage to component 20.

Step 210 may further comprise classifying the feature dissimilaritiesidentified in step 208. The inspection system 10 may include categoriesof damage or defect types for component 20. For example, damage may becategorized into classes such as warping, stretching, edge defects,erosion, nicks, cracks, and/or cuts. Step 210 may further compriseidentifying the damage type based on the dissimilarities between theinformation 30 and the reference model 22. Step 210 may further compriseclassifying the feature dissimilarities into categories of, for example,systemic damage or localized damage. Systemic damage may include warpingor stretching of component 20. Localized damage may include edgedefects, erosion, nicks, cracks, or cuts on a surface of component 20.Classifying the feature dissimilarities may be accomplished by, forexample, a Bayesian estimation, support vector machine (SVM), decisiontree, deep neural network, recurrent ensemble learning machine, or otherclassification method.

Step 212 may further comprise determining whether the feature differenceor dissimilarity represents damage to component 20. Step 212 maycomprise determining a probability of damage represented by the featuredissimilarity and/or classification. Step 212 may comprise determiningdamage by comparing the probability of damage to a threshold. Damage maybe determined if the probability meets or exceeds a threshold. Theinspection system 10 may determine if the damage is acceptable orunacceptable, and may determine if the component 20 should be acceptedor rejected, wherein a rejected component would indicate that thecomponent should be repaired or replaced.

Step 214 may further comprise storing, transmitting or displaying theinformation, feature differences or dissimilarities, classification ofthe feature differences or dissimilarities, a damage report, and/or adetermination or recommendation that the component 20 be accepted orrejected. Step 214 may further comprise displaying an image, a model, acombined image and 3D model, a 2D perspective from a 3D model, and thelike, of the damaged component for further evaluation by a user or by asubsequent automated system.

Referring also to FIG. 3 an exemplary automated optical inspectionsystem 10 can be seen. In another exemplary embodiment, the system 10can include an optical system for a gas turbine engine disk inspection.The component 20 can be a disk, a gear, and the like. The exemplaryembodiment shown in FIG. 3 includes a broached slot of a disk as thecomponent 20. The sensor 12 is shown as a camera system 12 configured tocapture images of disk 20. The camera system 12 can be fixed or mobile,such that the camera can move, pan, slide or otherwise reposition tocapture the necessary image data 14 of the disk 20. The camera system 12can comprise a focal plane array (FPA) 32 coupled to an aperture 34 andoptics 36 aligned to image the disk 20. In an exemplary embodiment, theoptics 36 can be a lens or lens system 38. In some embodiments it may bedesirable that camera system 12 resolve 0.2 mils/pixel (5 microns/pixel)with a depth of field of 1300 mils (33 millimeters). Using a typicalDSLR camera (Canon EOS 7D with an 18 Mpixels 14.9 mm×22.3 mm FPA using alens stopped down to f32 at a subject distance of 1 meter) would beinadequate because the depth of field would only be approximately 12 mm.Depth of field improves with subject distance, but resolution decreases.Depth of field improves with increasing f#, but available lightdecreases. A custom-designed optical system with a large FPA (50Mpixels) and longer standoff distance (1.65 meters), still at f32, mayachieve the desired performance. An equivalent structured-light 3D depthsensing system 12 has the same performance parameters and requires anequivalent custom design.

The inspection system 10 can include a processor 16 coupled to thecamera system 12. The processor 16 can be configured to determinedefects or damage to the gas turbine engine disk 20 based on videoanalytics. The processor 16 is shown with a transceiver configured tocommunicate wirelessly with the user interface 28. In another exemplaryembodiment the system can be hard wired. The processor 16 can beconfigured to automatically report damage and archive the damage fortrending and condition-based-maintenance.

The processor 16 can be configured to receive the data for the gasturbine engine disk 20 from the camera system 12. The processor 16 caninclude a Robust Principle Components Analysis program. The processor 16can include a low-order dynamic model of rotational motion duringinspection, and statistical image analysis to automatically detectpossible defects. The low-order dynamic model may be used to align(register) imagery taken at different rotation angles to achieve imageryof substantially the same appearance. The processor 16 can include aprogram configured to determine a low-rank part, (i.e., a model of acomponent without damage) by minimizing a matrix nuclear norm.

Referring also to FIG. 4 including 4 a-4 d, an image 40 of an exemplarybroached slot 42 can be seen at 4 a. FIG. 4b is a higher magnificationimage of the slot 42 taken from the image of 4 a. FIG. 4c shows imagesof the actual image 40 compared with a model 44 of an undamaged diskslot. As described above, the model 44 can be generated through varioustechniques. The image at 4 c also highlights portions of the broachedslot 42 that may exceed a limit as determined by the processor 16 andthe various programs for damage detection described above. FIG. 4dillustrates the exemplary statistical damage analysis 46. Thestatistical damage analysis 46 represents the empirical probabilitydensity function (pdf) of the minimum distance from an image pixel tothe model 44. Then the distance exceeds a threshold (two standarddeviations as depicted as “OK” in FIG. 4d ) the corresponding surface isdetermined to be damaged.

In an exemplary embodiment, 2D images from the camera system 12 can bereorganized into a 1D vector by concatenating successive columns of theimage. The resulting vector becomes one column of an image matrix D asexplained further below. Successive images, then, become successivecolumns of the image matrix D. Since an image typically has 1 millionpixels, or more, and the number of images taken while rotating thecomponent under inspection is typically only a few hundred or thousand,the matrix is typically much taller than it is wide.

Robust Principal Component Analysis (RPCA) can be applied to decomposean image matrix D into a nominally low-rank or “normal” matrixcomponent, A, and a sparse matrix component, E. The RPCA algorithm maybe applied according to the method in E. Candés, X. Li, Y. Ma, and J.Wright entitled “Robust principal component analysis?” (Journal of theACM, 58(3), May 2011). The matrix A captures the normal appearance ofthe broached slots 42, and the sparse matrix component E contains imagesof possible damage. The decomposition is formulated to minimize aweighted combination of a nuclear norm of the matrix A, and of the l₁norm of the sparse component, E according to Equations (1) and (2).

minimize ∥A∥*+λ∥E∥ ₁  (1)

subject to D=A+E  (2)

where: ∥A∥* denotes the nuclear norm of the matrix (i.e., sum of itssingular values); ∥E∥ denotes the sum of the absolute values of matrixentries; and λ is a parameter that balances rank and sparsity. In theexemplary embodiment described herein the “low-rank part” may, in fact,not actually low rank under the described circumstances. Nevertheless,the matrix A essentially captures a non-damage model of the componentunder inspection and the damaged component, if any, is in the residualmatrix E.

In an embodiment wherein 3D (depth) data from sensor(s) 12 comprises aframe of depth information arranged as 2-dimensional (u,v) depth matrix,the RPCA process may be used as described elsewhere herein. In analternative embodiment wherein the 3D (depth) data from sensor(s) 12comprises a frame of depth information arranged as a 3-dimensional(x,y,z) depth tensor, for example as an occupancy grid, a tensor-basedextension of the matrix-based RPCA process may be used. In this case,the sensor frames may be arranged as successive 3-dimensional sub-arraysof a 4-dimensional tensor. The 4-dimensional tensor may be decomposedinto a normal part (a linear combination of which may represent a normal3-dimensional model of a component) and an abnormal part which capturesdamage (any part of the data that is not representable by the normalpart). In an alternative embodiment, the 3-dimensional depth data may bereduced in dimension by successively appending columns of data along onedimension into a single long column. Performing this process reduces the3-dimensional frame to a 2-dimensional frame which may be used in theRPCA process described elsewhere herein.

There has been provided an automated optical inspection system. Whilethe automated optical inspection system has been described in thecontext of specific embodiments thereof, other unforeseen alternatives,modifications, and variations may become apparent to those skilled inthe art having read the foregoing description. Accordingly, it isintended to embrace those alternatives, modifications, and variationswhich fall within the broad scope of the appended claims.

What is claimed is:
 1. A system for component inspection comprising: atleast one sensor configured to capture sensor data of the component; anda processor coupled to said at least one sensor, said processorcomprising at least one model configured to separate said sensor datainto a normal category and an abnormal category.
 2. The system forcomponent inspection of claim 1, wherein said model comprises at leastone of a statistical model, an empirical model, a learned model, a priorcondition model, and a design model.
 3. The system for componentinspection of claim 1, further comprising a tangible, non-transitorymemory configured to communicate with said processor, the tangible,non-transitory memory having instructions stored therein that, inresponse to execution by the processor, cause the processor to performoperations comprising: receiving, by the processor, sensor data for saidcomponent from said at least one sensor; organizing, by the processor,the sensor data into a matrix, wherein each frame of said sensor datacomprises a single column in said matrix; separating, by the processor,said matrix into at least one of a low-rank part and a sparse part,wherein a linear combination of the low-rank part columns represents anundamaged component; and determining, by the processor, defects in thecomponent based on the sparse part.
 4. The system for componentinspection of claim 1, wherein said at least one sensor comprises anoptical system configured for high spatial resolution and large depth offield.
 5. The system for component inspection of claim 1, furthercomprising a tangible, non-transitory memory configured to communicatewith said processor, the tangible, non-transitory memory havinginstructions stored therein that, in response to execution by theprocessor, cause the processor to perform operations comprising:receiving, by the processor, sensor data for said component from said atleast one sensor; organizing, by the processor, the sensor data into atensor, wherein each frame of said sensor data comprises alower-dimensional portion in said tensor; separating, by the processor,said tensor into at least one of a normal part and an abnormal part,wherein a linear combination of the normal part represents an undamagedcomponent; and determining, by the processor, defects in the componentbased on the abnormal part.
 6. The system for component inspection ofclaim 1, wherein said at least one sensor comprises a depth sensingsystem configured for high spatial resolution and large range.
 7. Thesystem for component inspection of claim 1, wherein said processormodifies said sensor data according to a dynamic model of rotationalmotion during inspection.
 8. The system for component inspection ofclaim 1, wherein said processor comprises instructions selected from thegroup consisting of a Bayesian estimation, a support vector machine(SVM), a decision tree, deep neural network, recurrent ensemble learningmachine, and comparison to a threshold.
 9. The system for componentinspection of claim 1, wherein said component comprises radiallyarranged, substantially similar subcomponents.
 10. The system forcomponent inspection of claim 1, wherein said component is selected fromthe group consisting of a gas turbine engine disk, a vane assembly, agear, and a fan.
 11. A method for inspection of a component, comprising:aligning at least one sensor to capture sensor data of a component;coupling a processor to said at least one sensor, said processorcomprising at least one model; and separating said sensor data into anormal category and an abnormal category.
 12. The method for inspectionof a component of claim 11, wherein said processor performs operationscomprising: receiving sensor data for said component from said at leastone sensor; organizing the sensor data into a matrix, wherein each frameof said sensor data comprises a single column in said matrix; separatingsaid matrix into at least one of a low-rank part and a sparse part,wherein a linear combination of the low-rank part columns represents anundamaged component; and determining defects in the component based onthe sparse part.
 13. The method for inspection of a component of claim11, wherein said processor performs operations comprising: receivingsensor data for said component from said at least one sensors;organizing the sensor data into a tensor, wherein each frame of saidsensor data comprises a lower-dimensional portion in said tensor;separating said tensor into at least one of a normal part and anabnormal part, wherein a linear combination of the normal partrepresents an undamaged component; and determining defects in thecomponent based on the abnormal part.
 14. The method for inspection of acomponent of claim 11, wherein said at least one sensor comprises anoptical system configured for high spatial resolution and large depth offield.
 15. The method for inspection of a component of claim 12, whereinsaid at least one sensor comprises a depth sensing system configured forhigh spatial resolution and large range.
 16. The method for inspectionof a component of claim 11, wherein said at least one model comprises atleast one of a statistical model, an empirical model, a learned model, aprior condition model, and a design model.
 17. The method for inspectionof a component of claim 11, wherein said processor modifies said sensordata according to a dynamic model of rotational motion duringinspection.
 18. The method for inspection of a component of claim 11,wherein said processor comprises instructions selected from the groupconsisting of a Bayesian estimation, a support vector machine (SVM), adecision tree, deep neural network, recurrent ensemble learning machine,and comparison to a threshold.
 19. The method for inspection of acomponent of claim 11, wherein said component comprises radiallyarranged, substantially similar subcomponents.
 20. The method forinspection of a component of claim 11, wherein said component isselected from the group consisting of a gas turbine engine disk, a vaneassembly, a gear, and a fan.